The classification report in machine learning is used to evaluate the performance of a classification model. It provides a detailed summary of various metrics that help assess how well the model is performing. The key components of a classification report typically include:
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Precision: The ratio of true positive predictions to the total predicted positives. It indicates how many of the predicted positive cases were actually positive.
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Recall (Sensitivity): The ratio of true positive predictions to the total actual positives. It shows how many of the actual positive cases were correctly identified by the model.
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F1 Score: The harmonic mean of precision and recall. It provides a balance between the two metrics, especially useful when dealing with imbalanced datasets.
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Support: The number of actual occurrences of each class in the dataset.
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Accuracy: The overall correctness of the model, calculated as the ratio of correctly predicted instances to the total instances.
The classification report helps in understanding the strengths and weaknesses of the model, guiding further improvements and adjustments.
